Approximate Fully Connected Neural Network Generation

Tuba Ayhan; Mustafa Altun

doi:10.1109/SMACD.2018.8434843

Approximate Fully Connected Neural Network Generation

Tuba Ayhan
, Mustafa Altun

Elektronik ve Hab.Müh.Bölümü

Istanbul Technical University

Araştırma çıktısı: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › Hakem

10 Atıf (Scopus)

Özet

Approximate computing is exploited in implementation of fully connected networks for classification problems. A multiplier structure whose area is scalable over accuracy through approximate computing is proposed. In order to employ the multipliers in a network, an area reduction algorithm is formed. It can adjust the approximation level of multipliers while still maintaining the target classification performance, without prior information on the value of network weights. Implementing on a Spartan6 FPGA, up to 79% area saving is recorded for various performance targets.

Orijinal dil	İngilizce
Ana bilgisayar yayını başlığı	SMACD 2018 - 15th International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design
Yayınlayan	Institute of Electrical and Electronics Engineers Inc.
Sayfalar	93-96
Sayfa sayısı	4
ISBN (Basılı)	9781538651520
DOI'lar	https://doi.org/10.1109/SMACD.2018.8434843
Yayın durumu	Yayınlandı - 13 Ağu 2018
Etkinlik	15th International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design, SMACD 2018 - Prague, Czech Republic Süre: 2 Tem 2018 → 5 Tem 2018

Yayın serisi

Adı	SMACD 2018 - 15th International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design

???event.eventtypes.event.conference???

???event.eventtypes.event.conference???	15th International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design, SMACD 2018
Ülke/Bölge	Czech Republic
Şehir	Prague
Periyot	2/07/18 → 5/07/18

Bibliyografik not

Publisher Copyright:
© 2018 IEEE.

Finansman

ACKNOWLEDGEMENTS This project is funded by TUBITAK (The Scientific and Technological Research Council of Turkey), with the grant number 117E078.

Finansörler	Finansör numarası
TUBITAK
Türkiye Bilimsel ve Teknolojik Araştirma Kurumu	117E078

Belgeye Erişim

10.1109/SMACD.2018.8434843

Diğer Dosyalar ve Linkler

Link to publication in Scopus

Alıntı Yap

Ayhan, T., & Altun, M. (2018). Approximate Fully Connected Neural Network Generation. In SMACD 2018 - 15th International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design (pp. 93-96). Makale 8434843 (SMACD 2018 - 15th International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SMACD.2018.8434843

Ayhan, Tuba ; Altun, Mustafa. / Approximate Fully Connected Neural Network Generation. SMACD 2018 - 15th International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 93-96 (SMACD 2018 - 15th International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design).

@inproceedings{14875496cbbe4b25ae1f4232e901912d,

title = "Approximate Fully Connected Neural Network Generation",

abstract = "Approximate computing is exploited in implementation of fully connected networks for classification problems. A multiplier structure whose area is scalable over accuracy through approximate computing is proposed. In order to employ the multipliers in a network, an area reduction algorithm is formed. It can adjust the approximation level of multipliers while still maintaining the target classification performance, without prior information on the value of network weights. Implementing on a Spartan6 FPGA, up to 79\% area saving is recorded for various performance targets.",

keywords = "Approximate computing, area reduction, fully connected network",

author = "Tuba Ayhan and Mustafa Altun",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 15th International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design, SMACD 2018 ; Conference date: 02-07-2018 Through 05-07-2018",

year = "2018",

month = aug,

day = "13",

doi = "10.1109/SMACD.2018.8434843",

language = "English",

isbn = "9781538651520",

series = "SMACD 2018 - 15th International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "93--96",

booktitle = "SMACD 2018 - 15th International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design",

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Ayhan, T & Altun, M 2018, Approximate Fully Connected Neural Network Generation. in SMACD 2018 - 15th International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design., 8434843, SMACD 2018 - 15th International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design, Institute of Electrical and Electronics Engineers Inc., pp. 93-96, 15th International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design, SMACD 2018, Prague, Czech Republic, 2/07/18. https://doi.org/10.1109/SMACD.2018.8434843

Approximate Fully Connected Neural Network Generation. / Ayhan, Tuba; Altun, Mustafa.
SMACD 2018 - 15th International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design. Institute of Electrical and Electronics Engineers Inc., 2018. p. 93-96 8434843 (SMACD 2018 - 15th International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design).

Araştırma çıktısı: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › Hakem

TY - GEN

T1 - Approximate Fully Connected Neural Network Generation

AU - Ayhan, Tuba

AU - Altun, Mustafa

PY - 2018/8/13

Y1 - 2018/8/13

N2 - Approximate computing is exploited in implementation of fully connected networks for classification problems. A multiplier structure whose area is scalable over accuracy through approximate computing is proposed. In order to employ the multipliers in a network, an area reduction algorithm is formed. It can adjust the approximation level of multipliers while still maintaining the target classification performance, without prior information on the value of network weights. Implementing on a Spartan6 FPGA, up to 79% area saving is recorded for various performance targets.

AB - Approximate computing is exploited in implementation of fully connected networks for classification problems. A multiplier structure whose area is scalable over accuracy through approximate computing is proposed. In order to employ the multipliers in a network, an area reduction algorithm is formed. It can adjust the approximation level of multipliers while still maintaining the target classification performance, without prior information on the value of network weights. Implementing on a Spartan6 FPGA, up to 79% area saving is recorded for various performance targets.

KW - Approximate computing

KW - area reduction

KW - fully connected network

UR - https://www.scopus.com/pages/publications/85052540696

U2 - 10.1109/SMACD.2018.8434843

DO - 10.1109/SMACD.2018.8434843

M3 - Conference contribution

AN - SCOPUS:85052540696

SN - 9781538651520

T3 - SMACD 2018 - 15th International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design

SP - 93

EP - 96

BT - SMACD 2018 - 15th International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 15th International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design, SMACD 2018

Y2 - 2 July 2018 through 5 July 2018

ER -

Ayhan T, Altun M. Approximate Fully Connected Neural Network Generation. In SMACD 2018 - 15th International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design. Institute of Electrical and Electronics Engineers Inc. 2018. p. 93-96. 8434843. (SMACD 2018 - 15th International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design). doi: 10.1109/SMACD.2018.8434843

Approximate Fully Connected Neural Network Generation

Özet

Yayın serisi

???event.eventtypes.event.conference???

Bibliyografik not

Finansman

Belgeye Erişim

Diğer Dosyalar ve Linkler

Parmak izi

Alıntı Yap